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Bit- and Power Allocation in GMD and SVD-Based MIMO Systems

  • Andreas AhrensEmail author
  • Francisco Cano-Broncano
  • César Benavente-PecesEmail author
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 554)

Abstract

The singular value decomposition (SVD) is a popular technique used in multiple-input multiple-output (MIMO) systems to remove inter-antennas interferences in order to achieve the best performance. As a result, the MIMO channel is decomposed into a number of independent singular-input singular-output (SISO) channels with different weightings. In order to improve the performance, bit- and power-allocation strategies are required due to the unequal weighting coefficients. In contrast, the geometric mean decomposition (GMD) decomposes the MIMO channel into a number of equally weighted SISO channels with remaining inter-antenna interference which can be removed by using dirty paper precoding at the transmit side. Having equally weighted layers, the computational complexity required to implement bit- and power-allocation strategies decreases and GMD-based MIMO systems seem to be an appropriate solution. This paper analyses and compares the performance of SVD- and GMD-based MIMO systems affected by antennas correlation where QAM constellations are transmitted along the transmit antennas, demonstrating that the GMD-based one is more robust against antennas correlation. Furthermore, optimal and suboptimal bit- and power-allocation strategies are compared. This investigation demonstrates that the suboptimal solution provides a performance close to that offered by the optimal one but with a reduced computational cost.

Keywords

Multiple-input multiple-output System Singular-value decomposition Geometric mean decomposition Bit allocation Power allocation Antennas correlation Wireless transmission Tomlinson-Harashima precoding. 

References

  1. 1.
    Abdi, A., Kaveh, M.: A space-time correlation model for multielement antenna systems in mobile fading channels. IEEE J. Sel. Areas Commun. 20, 550–560 (2002)CrossRefGoogle Scholar
  2. 2.
    Benavente-Peces, C., Cano-Broncano, F., Ahrens, A., Ortega-Gonzalez, F., Pardo, J.: Analysis of singular values PDF and CCDF on receiver-side antennas correlated MIMO channels. Electron. Lett. 49(9), 625–627 (2013)CrossRefGoogle Scholar
  3. 3.
    Cano-Broncano, F., Ahrens, A., Benavente-Peces, C.: Iterative bit- and power allocation in correlated MIMO systems. In: International Conference on Pervasive and Embedded Computing and Communication Systems (PECCS), Lisboa, (Portugal), 7–9 January 2014Google Scholar
  4. 4.
    Chiani, M., Win, M., Zanella, A.: On the capacity of spatially correlated MIMO rayleigh-fading channels. IEEE Trans. Inf. Theory 49, 2363–2371 (2003)MathSciNetCrossRefzbMATHGoogle Scholar
  5. 5.
    Jiang, Y., Hager, W., Jian, L.: The generalized triangular decomposition. Math. Comput. 77, 1037–1056 (2008)CrossRefzbMATHGoogle Scholar
  6. 6.
    Jiang, Y., Li, J., Hager, W.: Joint transceiver design for MIMO communications using geometric mean decomposition. IEEE Trans. Signal Process. 53, 3791–3803 (2005)MathSciNetCrossRefGoogle Scholar
  7. 7.
    Loyka, S., Tsoulos, G.: Estimating MIMO system performance using the correlation matrix approach. IEEE Commun. Lett. 6, 19–21 (2002)CrossRefGoogle Scholar
  8. 8.
    Shiu, D.-S., Foschini, G.J., Gans, M., Kahn, J.: Fading correlation and its effect on the capacity of multi-element antenna systems. In: Universal Personal Communications (1998)Google Scholar
  9. 9.
    Yang, P., Xiao, Y., Yu, Y., Li, S.: Adaptive spatial modulation for wireless MIMO transmission systems. IEEE Commun. Lett. 15, 602–604 (2011)CrossRefGoogle Scholar
  10. 10.
    Zanella, A., Chiani, M.: Reduced complexity power allocation strategies for MIMO systems with singular value decomposition. IEEE Trans. Veh. Technol. 61, 4031–4041 (2012)CrossRefGoogle Scholar
  11. 11.
    Zheng, L.: Diversity and multiplexing: a fundamental tradeoff in multiple-antenna channels. IEEE Trans. Inf. Theory 49, 1073–1096 (2003)CrossRefzbMATHGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Department of Electrical Engineering and Computer Science, Communications Signal Processing GroupHochschule Wismar, University of Technology, Business and DesignWismarGermany
  2. 2.Department of Signal Theory and CommunicationsUniversidad Politécnica de Madrid, E.T.S. de Ingeniería y Sistemas de TelecomunicaciónMadridSpain

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